Inclusion and Belonging in Higher Education: A Scoping Study of Contexts, Barriers, and Facilitators
Bibliographic record
Abstract
A sense of inclusion and belonging are critical for students’ learning and personal development in higher education institutions. Learners who identify as non-majority identities (racial/ethnic minority, LGTBQ+, disability, and first generation) are at greater risk of feeling isolated and unwelcome. Lack of belonging and inclusion among individuals from marginalized identity groups is a contributor to increased stress as a chronic response to racism, stigmatization, discrimination, and exclusion. Conversely, a sense of inclusion and belonging contributes to better academic outcomes and enhanced physical and mental health. A systematic search of the literature initially yielded 2,914 articles with 68 eventually included for full-text analysis. Basic content analysis resulted in multiple categories including institutional context, barriers to inclusion and belonging, and facilitators of inclusion and belonging. The most commonly evoked institutional contexts were faculty and peer interaction; policies, procedures, and infrastructure; and classroom or clinical instruction. Barriers to inclusion and belonging included social exclusion, lack of accessibility, and microaggressions or other instances of discrimination and bias. Facilitators of inclusion and belonging included receptivity, availability of support services, accessible spaces, and inclusive policies and procedures. Lack of discussion regarding specifics of curriculum, instruction, and assessment indicate the need for future research to outline inclusive teaching best practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.024 | 0.029 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".